High risk for obstructive sleep apnea and other sleep disorders
among overweight and obese pregnant women
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Citation
Rice, Jayne R., Gloria T. Larrabure-Torrealva, Miguel Angel
Luque Fernandez, Mirtha Grande, Vicky Motta, Yasmin V.
Barrios, Sixto Sanchez, Bizu Gelaye, and Michelle A. Williams.
2015. “High risk for obstructive sleep apnea and other sleep
disorders among overweight and obese pregnant women.” BMC
Pregnancy and Childbirth 15 (1): 198. doi:10.1186/s12884-0150633-x. http://dx.doi.org/10.1186/s12884-015-0633-x.
Published Version
doi:10.1186/s12884-015-0633-x
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June 17, 2017 8:57:31 AM EDT
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Rice et al. BMC Pregnancy and Childbirth (2015) 15:198
DOI 10.1186/s12884-015-0633-x
RESEARCH ARTICLE
Open Access
High risk for obstructive sleep apnea and
other sleep disorders among overweight
and obese pregnant women
Jayne R. Rice1, Gloria T. Larrabure-Torrealva2,3, Miguel Angel Luque Fernandez1, Mirtha Grande5, Vicky Motta2,
Yasmin V. Barrios1, Sixto Sanchez4,5, Bizu Gelaye1* and Michelle A. Williams1
Abstract
Background: Obstructive sleep apnea (OSA), a common and serious disorder in which breathing repeatedly stops
during sleep, is associated with excess weight and obesity. Little is known about the co-occurrence of OSA among
pregnant women from low and middle-income countries.
Methods: We examined the extent to which maternal pre-pregnancy overweight or obesity status are associated with
high risk for OSA, poor sleep quality, and excessive daytime sleepiness in 1032 pregnant women in Lima, Peru. The
Berlin questionnaire was used to identify women at high risk for OSA. The Pittsburgh Sleep Quality Index (PSQI) and
Epworth Sleepiness Scale (ESS) were used to examine sleep quality and excessive daytime sleepiness, respectively.
Multinomial logistic regression procedures were employed to estimate odds ratios (aOR) and 95 % confidence intervals
(CI) adjusted for putative confounding factors.
Results: Compared with lean women (<25 kg/m2), overweight women (25–29.9 kg/m2) had 3.69-fold higher
odds of high risk for OSA (95 % CI 1.82–7.50). The corresponding aOR for obese women (≥30 kg/m2) was 13.23
(95 % CI: 6.25–28.01). Obese women, as compared with their lean counterparts had a 1.61-fold higher odds of
poor sleep quality (95 % CI: 1.00–2.63).
Conclusion: Overweight or obese pregnant women have increased odds of sleep disorders, particularly OSA.
OSA screening and risk management may be indicated among pregnant women in low and middle income
countries, particularly those undergoing rapid epidemiologic transitions characterized by increased prevalence
of excessive adult weight gain.
Background
Obesity continues to be one of the fastest growing metabolic conditions worldwide. According to the World
Health Organization (WHO) the prevalence of obesity is
estimated to be approximately 12 % making it one of the
21st century epidemic disease [1]. The prevalence of
obesity in Andean Latin American countries has almost
doubled from 9 % in 1980 to 17 % in 2008 [2]. Recent
studies conducted in Peru found that more than 40 % of
the adult population in the metropolitan Lima is overweight or obese [3]. Excess consumption of energy dense
foods and lack of physical activity have been implicated
* Correspondence: [email protected]
1
Department of Epidemiology, Harvard T.H. Chan School of Public Health,
677 Huntington Ave, K501, Boston, MA, 02115 USA
Full list of author information is available at the end of the article
as reasons for the rise in obesity prevalence in Peru and
elsewhere. Major obesity-related chronic disorders
include: cardiovascular diseases, diabetes, hypertension,
and breathing difficulties [1]. During pregnancy, maternal
overweight or obese status are associated with adverse
perinatal and neonatal outcomes such as gestational
diabetes [4], pregnancy induced hypertension, preeclampsia [5], cesarean delivery [6], miscarriage [7], large for
gestational age [4], macrosomia [8], and stillbirth [9].
There is a growing body of evidence that documents
the impact of obesity on sleep disorders, more specifically obstructive sleep apnea (OSA) [2, 10, 11]. OSA is a
condition characterized as repeated episodes of complete
or total blockage of the upper airway during sleep
[12, 13]. Snoring, persistent daytime sleepiness, and
© 2015 Rice et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Rice et al. BMC Pregnancy and Childbirth (2015) 15:198
periods of awakening out of breath during the night are
hallmark symptoms of OSA [13]. Epidemiologic studies
have shown that OSA and poor sleep quality are independently associated with weight gain, cardiometabolic
disorders, cognitive impairments, hypertension, psychiatric disorders and headaches [2, 14]. Accumulating
evidence also documents a strong, positive and bidirectional association of obesity with high risk for OSA
and other sleep disorders [2, 15]. Investigators have argued
that increased fat deposits around the upper airways of
overweight and obese individuals can produce obstruction
of breathing, reducing the flow of oxygen leading to sleep
apnea [16]. Conversely, OSA can contribute to obesity
because fragmented and non-restorative sleep related to
sleep apnea has been associated with increased caloric intake [15]. Sleep patterns are known to change throughout
pregnancy in part due to physiological and hormonal
changes [17]. Studies have reported increased snoring and
narrower upper airways during the third trimester of
pregnancy compared to postpartum [18].
Despite the high prevalence of sleep disorders among
pregnant women [19] and despite a well-established
body of evidence showing elevated BMI as a significant
predictor of sleep disorders [20], few studies have investigated the risk of sleep disorders among overweight and
obese pregnant women. To fill this void in the literature,
we sought to assess the extent to which pre-gestational
overweight and obesity status are associated with increased odds of high risk of OSA and other sleep disorders among pregnant women in Peru.
Methods
Participants, sample size and study setting
This study was conducted among pregnant women attending prenatal care clinics at the Instituto Nacional
Materno Perinatal (INMP) in the city of Lima, Peru
between February 2013 and March 2014. The INMP,
overseen by the Peruvian Ministry of Health, is the primary referral hospital for maternal and perinatal care.
Eligible women were 18 years of age or older, could
speak and read Spanish, and with a gestational age between 24 to 28 weeks. Enrolled participants were invited
to participate in an interview where trained research
personnel used a structured questionnaire to elicit information regarding maternal socio-demographic, lifestyle
characteristics, medical and reproductive histories, and
sleep characteristics. Anthropometric measures and vital
signs were measured by experienced midwives. Women
were weighed in light clothing using the WHO standard
guidelines. All participants provided written informed
consent and study procedures were approved by institutional review boards of the INMP, Lima, Peru and the
Harvard T.H. Chan School of Public Health Office of
Human Research Administration, Boston, MA, USA.
Page 2 of 8
Instruments and variable specification
The Berlin Questionnaire originated from Berlin, Germany
is a widely used and validated screening instrument for
assessing high risk for obstructive sleep apnea (OSA). The
questionnaire consists of 11 questions separated into three
sections [21, 22]. Section 1 asked participants whether they
snore. Those who responded affirmatively were asked how
loud their snoring was, how often it occurred, and whether
their snoring bothered other people. In the present study,
participants were also asked whether anyone has ever
noticed cessation of their breathing during sleep. Section 2
asked participants how often they felt tired or fatigued
right after sleep, how often they felt tired, fatigued, or not
up to par during wake time, and whether they ever fall
asleep driving a car. In section 3, participants were asked
about a history of hypertension, as well as their height,
weight, and age. A section was considered positive if there
were two affirmative answers in either section 1 or 2, or
one affirmative response in section 3. In section 3, high
risk for OSA was defined when there was a history of
hypertension or obesity.
The Berlin questionnaire is widely used in pregnancy
[23–26]. For the purposes of this study when 2 or more
sections were classified as positive, the participant was
deemed to be at high risk for OSA [21, 22]. In addition,
given the lack of consensus concerning the utility of this
diagnostic criteria in pregnancy [23–25], we evaluated
the extent to which snoring (i.e., those positive for
section 1) is associated with maternal obesity status.
Sleep quality was evaluated using the Pittsburgh Sleep
Quality Index (PSQI), a 19-item self-reported questionnaire that assesses sleep quality over the past month
[27]. The PSQI has seven sleep components: sleep duration, disturbance, latency, habitual sleep efficiency, use
of sleep medicine, daytime dysfunction due to sleepiness
and overall quality of sleep. Each component produced a
score ranging from 0 to 3, where a score of 3 indicates
the highest level of dysfunction. A global sleep quality
score is obtained by summing the individual component
scores (range 0 to 21) with higher scores indicative of
poorer sleep quality during the previous month. Participants with global scores that exceed 5 are classified as
poor sleepers [27]. Those with a score of 5 or less were
classified as good sleepers. This classification system is
consistent with prior studies of pregnancy including
those conducted in Peru [28–30].
Daytime sleepiness was measured using the Epworth
Sleep Scale (ESS) [31]. The instrument has been widely
validated globally including in Peru [32]. The ESS is an
8-item questionnaire capturing an individual’s propensity
to fall asleep during commonly encountered situations
on a scale from 0 to 3. Overall scores range between 0
and 24. ESS scores of 10 or higher are indicative of excessive daytime sleepiness [31].
Rice et al. BMC Pregnancy and Childbirth (2015) 15:198
Other covariates
Maternal age at the time of interview was categorized as
follows: 18–20, 20–29, 30–34, and ≥35 years. Other
sociodemographic variables were categorized as follows:
maternal and paternal educational attainment (≤6, 7–12,
and >12 completed years of schooling); marital status
(married and living with partner vs. others); access to
basic foods (very hard/hard, somewhat hard, not very
hard); food insecurity (yes vs. no); access to medical care
(very hard/hard, somewhat hard, not very hard); and
parity (nulliparous vs. multiparous). Pre-pregnancy body
mass index was calculated as weight (in kilograms) divided
by the square of height (in meters and used to identify
lean (BMI < 25 kg/m2), overweight (BMI: 25–29.9 kg/m2),
and obese (BMI ≥ 30 kg/m2) women according to the
World Health Organization (WHO) criteria.
Statistical analysis
We examined the distributions of maternal sociodemographic, reproductive, and medical characteristics according to pre-pregnancy BMI categories. Multivariate
multinomial logistic regression models were fitted to estimate adjusted odds ratios [33] and 95 % confidence intervals (95 % CI) of sleep disorders in relation to
maternal overweight and obesity status after adjusting
for potential confounders. Separate models were fitted
for each sleep complaint. In multivariable models we adjusted for maternal age, educational attainment, marital
status, and parity. Additional adjustment for the other
covariates listed in Table 1 did not substantially change
odds ratios. We explored the possibility of a nonlinear
relation between BMI and odds of high risk for OSA,
using generalized additive logistic regression modeling
procedures (GAM). Finally, on the basis of study findings reporting limited clinical utility of the total score of
the Berlin questionnaire in pregnancy [25], we conducted post-hoc analyses to examine the extent to which
maternal overweight and obesity status are associated
with increased odds of snoring (as assessed using information from section 1 of the Berlin questionnaire). All
analyses were performed using Stata 12.0 statistical software (Stata, College Station, TX). The GAM analyses
were performed using “R” software version 3.1.2. All reported p-values are two-tailed and deemed statistically
significant at α = 0.05.
Results
The socio-demographic characteristics of the participants
are presented in Table 1. A total of 1032 pregnant women
between the ages of 18 and 45 years (mean age = 28.6 years,
standard deviation = 6.2 years) participated in the study.
The majority of participants were married or living with
their partner (87.6 %) while more than half (55.5 %)
reported an education attainment of 7 or more years.
Page 3 of 8
Table 1 Maternal socio-demographics characteristics
Total sample (N = 1,032)
Characteristic
n (%)
Maternal Age (years)
<19
42 (4.1)
20–29
551 (53.4)
30–34
237 (23.0)
≥35
202 (19.5)
Maternal Age (years) Mean (SD) [Min, Max]
28.6 (6.2) [18, 45]
Marital Status
Married or living with partner
903 (87.6)
Single or living alone/divorced
128 (12.4)
Maternal Education (years)
>12
32 (3.10)
7–12
541 (52.4)
≤6
459 (44.5)
Paternal Education (years)
>12
23 (2.2)
7–12
566 (55.4)
≤6
433 (42.4)
Pre-pregnancy BMI (kg/m2)
<25 (Normal weight)
573 (55.5)
25–29.9 (Overweight)
350 (33.9)
≥30 (Obese)
109 (10.6)
Difficulties to Pay for Basics
Very hard/hard
153 (14.9)
Somewhat hard
351 (34.1)
Not very hard
526 (51.1)
Food Insecurity
No
369 (35.8)
Yes
663 (64.2)
Difficulties to Access Medical Care
Very hard/hard
199 (19.3)
Somewhat hard
714 (69.3)
Not very hard
118 (11.4)
Approximately 50 % of participants reported having difficulty paying for basics (48.9 %), while 64.2 % reported food
insecurities and more than three fourth (88.6 %) reported
difficulties to accessing medical care.
Table 2 shows the relationship between pre-pregnancy
BMI and sleep disorders. The prevalence of high risk for
OSA was 2.1 %, 8.0 % and 25.7 % for lean, overweight
and obese study participants, respectively. The corresponding prevalence for poor sleep quality were 19.0 %,
21.4 %, and 28.4 %. Generally similar prevalence of excessive daytime sleepiness was noted among lean
(12.7 %), overweight (13.1 %) and obese (12.9 %) study
Rice et al. BMC Pregnancy and Childbirth (2015) 15:198
Page 4 of 8
Table 2 Odds of obstructive sleep apnea, poor sleep quality, and excessive daytime sleepiness in relation to pre-pregnancy body
mass index (BMI)
Pre-pregnancy BMI in kg/m2
Berlin Questionnaire
Unadjusted OR (95 % CI)
BMI <25
(N = 573)
BMI 25–29.9
(N = 350)
BMI ≥ 30
(N = 109)
n (%)
n (%)
n (%)
BMI 25–29.9
*
Adjusted OR (95 % CI)
BMI ≥30
BMI 25–25.9
BMI ≥30
Low risk OSA
561 (97.9)
322 (92.0)
81 (74.3)
Reference
Reference
Reference
Reference
High risk OSA
12 (2.1)
28 (8.0)
28 (25.7)
4.06 (2.04–8.10)
16.2 (7.90–33.04)
3.69 (1.82–7.50)
13.23 (6.25–28.01)
No (PSQI ≤5)
464 (81.0)
275 (78.6)
78 (71.6)
Reference
Reference
Reference
Reference
Yes (PSQI >5)
109 (19.0)
75 (21.4)
31 (28.4)
1.20 (0.83–1.61)
1.70 (1.06–2.70)
1.16 (0.82–1.63)
1.61 (1.00–2.63)
Poor Sleep Quality
Excessive Daytime Sleepiness
No (ESS <10)
500 (87.3)
304 (86.9)
95 (87.1)
Reference
Reference
Reference
Reference
Yes (ESS ≥10)
73 (12.7)
46 (13.1)
14 (12.9)
1.03 (0.70–1.54)
1.00 (0.55–1.86)
1.06 (0.70–1.60)
1.07 (0.57–2.03)
*
Adjusted for maternal age, education, marital status and parity; Reference group: BMI < 25
OR Odds ratio; CI Confidence Interval; OSA obstructive sleep apnea; PSQI Pittsburgh sleep quality index; ESS Epworth sleepiness scale
participants. In general, as pre-pregnancy BMI increased,
the odds of sleep disorders increased. After adjusting for
confounders compared with normal weight women
(<25 kg/m2), overweight women (25–29.9 kg/m2) had
3.69-fold higher odds of experiencing high risk for
OSA (assessed using the Berlin questionnaire) (95 %
CI: 1.82–7.50). Obese women (≥30 kg/m2) had a 13.2fold higher odds of experiencing high risk for OSA
(aOR = 13.23; 95 % CI: 6.25–28.01) as compared with
their lean counterparts. Additionally the odds of high
risk for OSA was modeled in relation to BMI expressed
as continuous variable using procedures based on a
general additive model. Results from these analyses
confirmed a linear relationship between increasing
BMI and the odds of high risk for OSA (Fig. 1).
Compared with lean women, overweight women were
associated with modest elevated, and statistically nonsignificant, odds of poor sleep quality (aOR = 1.16; 95 % CI:
0.82–1.63). However, obese women had a statistically significant 1.61-fold increased odds of poor sleep quality
(95 % CI: 1.00–2.63), as compared with lean women
(Table 2). We observed no clear evidence of an association
of pre-pregnancy BMI and excessive daytime sleepiness.
In post-hoc analyses restricted to the snoring components
of the Berlin questionnaire (section 1), we found that
compared with lean women, obese (aOR = 1.83; 95 % CI:
1.03–3.24) and overweight women (aOR = 1.20; 95 %CI:
0.78–1.83) were more likely to report snoring during
pregnancy.
Discussion
Overall, we found that pregnant women who are overweight and obese have increased odds of sleep disorders. Compared with lean women (<25 kg/m2),
overweight women (25–29.9 kg/m2) had 3.69-fold
higher odds of high risk for OSA (95 % CI 1.82–7.50).
The corresponding OR for obese women (≥30 kg/m2)
was 13.23 (95 % CI: 6.25–28.01). Obese women, as
compared with their lean counterparts had a 1.61-fold
higher odds of poor sleep quality (95 % CI: 1.00–2.63).
In the present study, the prevalence estimates of high
risk for OSA were 6.5 % (assessed using Berlin questionnaire). The prevalence of high risk for OSA found in our
study is lower than estimates from other studies. In their
study among predominantly Hispanic pregnant women in
Houston, Texas Antony et al. found a 15.5 % prevalence
of high risk for OSA assessed using Berlin questionnaire
[4]. The investigators further noted that obesity was associated with a 9-fold increased odds (95 % CI 4.68–17.39)
of high risk for OSA compared with normal weight
women in that population. In a study of 276 pregnant
women in Korea, Ko et al. [20] found a high prevalence of
OSA in obese women (43.6 %) compared with non-obese
women (32.6 %) (p = 0.001). Their findings indicating increased odds of high risk for OSA among overweight and
obese pregnant women are in general agreement with our
study findings. Other studies conducted among men
and non-pregnant women [34–36] report findings that
are in agreement with those reported by our team and
others [4, 20]. For example, using data from the 2007
Sleep in America Poll of the National Sleep Foundation,
Kapsimalis and Kryger [37] noted that the prevalence
of high risk for OSA (determined using Berlin questionnaire) was 8.5 % among women with normal BMI while
the prevalence estimates were markedly higher among
overweight (21 %) and obese (62 %) women. In a
recently published study of college students in Chile,
Wosu et al. [15] reported, that obese students were 8.26
times-as likely to experience high risk for OSA compared
with normal weight students (95 % CI:4.59–14.86) after
adjusting for confounders. Another finding that merits
consideration is our results that showed obese pregnant
Rice et al. BMC Pregnancy and Childbirth (2015) 15:198
Log odds of high risk for OSA
Page 5 of 8
Body Mass Index (kg/m2)
Fig. 1 Relation between log odds of high risk for obstructive sleep apnea and pre-pregnancy body mass index (solid line) with 95 % confidence
interval (dotted lines)
women were 1.83-time as likely to report snoring
(aOR = 1.83; 95 % CI: 1.03–3.24) during pregnancy as
compared with lean women. However, this association
did not reach statistical significance for overweight
women (aOR = 1.20; 95 % CI: 0.78–1.83). Investigators
have documented similar findings and have speculated
that pregnant women are more likely to have higher
BMI while carrying a lesser proportion of body fat and
therefore more likely to meet the criteria of obesity
used by the Berlin questionnaire [23]. Taken together,
the current data indicate that the Berlin questionnaire
might have limited utility in OSA screening due to the
inclusion of obesity as one of the classification criteria.
In the last 50 years, the average self-reported sleep
duration in the United States has decreased by 1.5 to 2 h
while the prevalence estimates of obesity and diabetes
have increased with Hispanics and Blacks showing
marked increase [38, 39]. A large literature primarily focused on men and non-pregnant women has shown that
obesity is related to sleep insufficiency and poor sleep
quality [40]. For instance, Chaput et al. (2007) in Canada
found that women with short sleep duration (5 to 6 h)
were 1.69-times as likely (OR = 1.69; 95 % CI: 1.15–2.39)
to be overweight or obese compared with normal
sleepers (7 to 8 h) [41]. In Taiwan Hung et al. (2012)
noted that being overweight or obese was statistically
significantly associated with increased global PSQI
scores (p < 0.001) [40]. Logue et al. (2014) in an urban
family medicine center in the US reported a statistically
significant association of poor sleep quality and obesity
(p = 0.005) independent of age, gender, and ethnicity
[42]. It is well established that sleep is altered during
pregnancy [43]. Of note, in the second and third trimesters, pregnant women are more likely to have frequent
awakenings due to fetal movements, discomfort, backaches
as well as frequent urge to urinate due to an enlarged
uterus [44] contributing to sleep insufficiency and fragmentation. Poor material sleep quality and other sleep disorders influence not only the mother but also their
offspring to adverse cardio metabolic pathology later in
life.
In the current study, we found no evidence of an association between BMI and excessive daytime sleepiness
after adjustment for possible confounders. Dixon et al.
(2007), in their study among 1055 Australian patients
presenting for obesity surgery, also found no statistically
significant association between ESS scores and BMI [45].
We do not have an explanation for these null findings
although it is important to note that the ESS is a global
summary score of questions on the risk of daytime
sleepiness during different situations during the daytime.
The ESS was originally developed to measure one construct—excessive daytime sleepiness [27, 31]. However,
an emerging literature has shown that the eight items of
ESS do not necessarily assess a unidimensional construct
rather two or three different aspects of daytime sleepiness [46]. Hence, the summary score of the all eight
items may not be the best index of excessive day time
sleepiness. Future studies should look at how individual
items might be influenced by obesity/overweight status.
Several plausible and compelling biological mechanisms have been proposed to explain the observed
obesity/overweight-sleep disorder associations. Obesity
can contribute to many physiological changes that increase the risk of OSA. For example, excessive soft tissue
due to obesity, may narrow the pharyngeal airway and
reduce lung volume [2]. Additionally, pregnancy induces
many physiological changes. These includes enlargement
of the uterus which can elevate the diaphragm and alter
respiration. These alterations, for instance, may increase
Rice et al. BMC Pregnancy and Childbirth (2015) 15:198
the tendency for collapsing the upper airway during
sleep [19]. Changes in hormones also predispose pregnant women to episodes of sleep apnea. Notably,
increased estrogen concentration has been linked to mucosal edema, and progesterone has been linked to increase respiratory center sensitivity to CO2, therefore
causing instability of the respiratory control mechanism
[47]. Collectively, these physiological and hormonal
changes may interact to increase the obesity-sleep disorders associations.
Our study has several notable strengths including a
relatively large sample size, the use of cross-culturally
accepted instruments to characterize sleep disorders and
rigorously trained research staff administering the
questionnaires. However, several important limitations
must be considered when interpreting our findings. First,
since this was a cross-sectional study, we cannot delineate
the temporal relationship between maternal elevated BMI
and sleep disorders. We also cannot determine whether
sleep disturbances may be attributed to pregnancy related
physiological alterations. Second, our findings are based
on a population of pregnant women seeking care at a
specialized maternal hospital; hence, caution is needed before generalizing the results to other populations. Third,
although we used multiple validated questionnaires, these
questionnaires may have contributed to some errors in
the classification of participants’ sleep disturbances [20].
Use of screening questionnaires for assessing sleep disorders in pregnancy is an important component of sleep
health research. The success of screening however, is
largely dependent on the accuracy of the questionnaires
and use diagnostic procedures, used as the gold standard,
to evaluate the effectiveness of screening questionnaires.
The gold standard for documenting many sleep disorders
including OSA is in-laboratory polysomnography (PSG)
assessment. Unfortunately due to cost, complexity and
participant burden, use of PSG testing is limited in largescale epidemiologic studies. Thus use of screening questionnaires with low specificity remains the main modality
for ascertaining OSA. Future studies are warranted to develop, refine and enhance the psychometric properties of
screening questionnaires and improve their utility for early
identification of sleep disorders in pregnancy. The fact
that our findings are similar to those that used more invasive, though objective measures of sleep traits (e.g., polysomnography) [48] serve to attenuate some concerns.
Available literature suggests that maternal OSA may be
associated with an increased risk of adverse perinatal outcomes [47, 49]. For instance in a meta-analysis of 9795
participants Luque-Fernandez et al. [49] found women
with sleep disordered breathing had a 3-fold increased risk
of gestational diabetes (OR = 3.06; 95 % CI: 1.89–4.96).
Chen et al. [50] found that mothers with OSA were more
likely to have low birth weight, preterm birth, and small
Page 6 of 8
for gestational age newborns, cesarean section, preeclampsia, gestational diabetes, and gestational hypertension as
compared with unaffected mothers. Louis et al. [51]
reported that OSA was associated with an increased risk
of preterm delivery and maternal morbidity. Additionally,
Kamysheva et al. have reported increased odds of antepartum and postpartum depression among women with
symptoms of poor sleep [52].
Conclusion
In conclusion, we observed increased risks of sleep disorders amongst overweight and obese pregnant Peruvian
women. These observations, when coupled with earlier
report, have important clinical and public health implications because pregnant women with symptoms of OSA
are at higher risk of adverse pregnancy and perinatal outcomes [47]. Collectively, our findings and those of others
[14] underscore the clinical and public health implications
for OSA screening and treatment among reproductive age
and pregnant women in low and middle income countries,
particularly those undergoing rapid epidemiologic transitions characterized by increased prevalence of excessive
adult weight gain.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
MAW conceived and designed the study. JRR, MALF, BG and MAW analyzed
data. JRR, BG and MAW drafted the manuscript. All authors interpreted the
data, critically revised the draft for important intellectual content, and gave
final approval of the manuscript to be published.
Acknowledgment
This research was supported by Roche Diagnostic Operations Inc. (project
number 208617–5074547) and the National Institutes of Health (NIH),
National Institute for Minority Health and Health Disparities (T37-MD000149).
The funders had no role in study design; in the collection, analysis and
interpretation of data; in the writing of the report; and in the decision to
submit the paper for publication. The authors wish to thank the dedicated
staff members of Asociacion Civil Proyectos en Salud (PROESA), Peru and
Instituto Especializado Materno Perinatal, Peru for their expert technical
assistance with this research.
Author details
1
Department of Epidemiology, Harvard T.H. Chan School of Public Health,
677 Huntington Ave, K501, Boston, MA, 02115 USA. 2Instituto Nacional
Materno Perinatal de Lima, Lima, Peru. 3Departamentos de Medicina y
Ginecología y Obstetricia Universidad Nacional Universidad Nacional Mayor
de San Marcos, Lima, Peru. 4Universidad de Ciencias Aplicadas, Lima, Peru.
5
Asociación Civil de Proyectos en Salud, AC.PROESA, Lima, Peru.
Received: 10 February 2015 Accepted: 21 August 2015
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